Predicting Cancer Disease Using KNN, J48 and Logistic Regression algorithm.

EasyChair Preprint no. 1538

Abstract

In Bangladesh, cervical cancer is the 2^nd numerous well-known cancer in women. It is evaluated that every year 11,956 unique possibilities of cervical cancer exist identified in Bangladesh and 6582 women die of the disease. The impartial of this investigation is to examine the motivational features to work with cancer patients, their consequences in job well-being among Portuguese healthcare specialists and to recognize the position of socio demographic and industrial variables on motive and job contentment. Weka medium has been used for mapping the accuracy of the cancer disease dataset including 20 sorts of cancer. Weka tool is utilized to estimate the exactness of the cancer disease dataset including 20 sorts of cancer. In (KNN) k Nearest Neighbour the accuracy is 97.1%, J48 accuracy is 97.8% and Logistic Regression accuracy is 98.2%.

Keyphrases: Classification, different cancers, J48, KNN, logistic regression

@Booklet{EasyChair:1538,
  author = {Ariful Islam Bhuiyan and Tajul Islam and Taufik Akunjee and Rafiqul Islam and Md. Hashikul Islam},
  title = {Predicting Cancer Disease Using KNN, J48 and Logistic Regression algorithm.},
  howpublished = {EasyChair Preprint no. 1538},

  year = {EasyChair, 2019}}